U.S. patent application number 16/005957 was filed with the patent office on 2019-12-12 for alerting an offline user of a predicted computer file update.
The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Liam S. Harpur, John RICE, Asima Silva.
Application Number | 20190377566 16/005957 |
Document ID | / |
Family ID | 68763882 |
Filed Date | 2019-12-12 |
United States Patent
Application |
20190377566 |
Kind Code |
A1 |
Harpur; Liam S. ; et
al. |
December 12, 2019 |
ALERTING AN OFFLINE USER OF A PREDICTED COMPUTER FILE UPDATE
Abstract
The method, computer program product and computer system may
include a computing device which may receive a copy of a master
digital, which may include metadata, file from a server. The
computing device may analyze the metadata of the master digital
file for a pattern of updates initiated by one or more users using
one or more computing devices. The computing device may generate a
file update prediction for discouraging use of an outdated version
of the master digital file. The file update prediction may indicate
an estimation for when the master digital file is likely to be
updated based on the pattern of updates. The computing device may
generate an alert for display offline on a user interface. The
alert may indicate the estimation for when the master digital will
be updated by the one or more users.
Inventors: |
Harpur; Liam S.; (Dublin,
IE) ; RICE; John; (Tramore, IE) ; Silva;
Asima; (Holden, MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
Armonk |
NY |
US |
|
|
Family ID: |
68763882 |
Appl. No.: |
16/005957 |
Filed: |
June 12, 2018 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 67/34 20130101;
H04L 67/26 20130101; G06F 16/2372 20190101; G06F 8/65 20130101 |
International
Class: |
G06F 8/65 20060101
G06F008/65; H04L 29/08 20060101 H04L029/08; G06F 17/30 20060101
G06F017/30 |
Claims
1. A method for predicting a digital file update, the method
comprising: receiving, by a first computing device, a copy of a
master digital file from a server for use offline, wherein the
master digital file includes metadata, and wherein the master
digital file is a digital file shared between one or more users;
analyzing the metadata of the master digital file for a pattern of
updates initiated by a user on the first computing device and one
or more users using one or more second computing devices,
respectively, the pattern of updates indicating a regular time
interval the master digital file is updated; generating a file
update prediction for discouraging offline use of an outdated
version of the master digital file, the file update prediction
indicating an estimation for when the master digital file is likely
to be updated based on the pattern of updates; and generating an
alert for display offline on a user interface of the first
computing device, the alert indicating the estimation for when the
master digital file will be updated by the one or more users, to
discourage and avoid use of the outdated version of the master
digital file by the user.
2. A method as in claim 1, further comprising: analyzing
applications resident on the first computing device and the one or
more second computing devices for user data of the one or more
users; and updating the pattern of updates if the user data of the
one or more users indicates the pattern of updates will be
interrupted.
3. A method as in claim 2, wherein the user data includes user
application data from at least one of the group of user
applications consisting of: a user calendar, a user social media
account, or a user schedule.
4. A method as in claim 1, wherein the alert is a time estimation
comprising a countdown clock to when the master digital file will
be updated by the one or more users.
5. A method as in claim 1, wherein the metadata includes
information as to when the master digital file has been updated and
the one or more users who updated the master digital file.
6. A method as in claim 1, wherein the alert is a visual
notification.
7. A method as in claim 1, wherein analyzing the metadata of the
master digital file for a pattern of updates initiated by a user on
the first computing device and one or more users using one or more
second computing devices, respectively, uses linear discriminant
analysis to determine clusters of activity based on interactions by
the one or more users with the master digital file.
8. A computer program product for file update prediction, the
computer program product comprising: a computer-readable storage
medium having program instructions embodied therewith, wherein the
computer readable storage medium is not a transitory signal per se,
the program instructions executable by a computer to cause the
computer to perform a method, comprising: receiving, by a first
computing device, a copy of a master digital file from a server for
use offline, wherein the master digital file includes metadata, and
wherein the master digital file is a digital file shared between
one or more users; analyzing the metadata of the master digital
file for a pattern of updates initiated by a user on the first
computing device and one or more users using one or more second
computing devices, respectively, the pattern of updates indicating
a regular time interval the master digital file is updated;
generating a file update prediction for discouraging offline use of
an outdated version of the master digital file, the file update
prediction indicating an estimation for when the master digital
file is likely to be updated based on the pattern of updates; and
generating an alert for display offline on a user interface of the
first computing device, the alert indicating the estimation for
when the master digital will be updated by the one or more users to
discourage and avoid use of the outdated version of the master
digital file by the user.
9. A computer program product as in claim 8, further comprising:
analyzing applications resident on the first computing device and
the one or more second computing devices for user data of the one
or more users; and updating the pattern of updates if the user data
of the one or more users indicates the pattern of updates will be
interrupted.
10. A computer program product as in claim 9, wherein the user data
includes user application data from at least one of the group of
user applications consisting of: a user calendar, a user social
media account, or a user schedule.
11. A computer program product as in claim 8, wherein the alert is
a time estimation comprising a countdown clock to when the master
digital file will be updated by the one or more users.
12. A computer program product as in claim 9, wherein the metadata
includes information as to when the master digital file has been
updated and the one or more users who updated the master digital
file.
13. A computer program product as in claim 8, wherein the alert is
a visual notification.
14. A computer program product as in claim 8, wherein analyzing the
metadata of the master digital file for a pattern of updates
initiated by a user on the first computing device and one or more
users using one or more second computing devices, respectively,
uses linear discriminant analysis to determine clusters of activity
based on interactions by the one or more users with the master
digital file.
15. A system for file update prediction, the system comprising: a
computer system comprising, a processor, a computer readable
storage medium, and program instructions stored on the computer
readable storage medium being executable by the processor to cause
the computer system to: receive, by a first computing device, a
copy of a master digital file from a server for use offline,
wherein the master digital file includes metadata, and wherein the
master digital file is a digital file shared between one or more
users; analyze the metadata of the master digital file for a
pattern of updates initiated by a user on the first computing
device and one or more users using one or more second computing
devices, respectively, the pattern of updates indicating a regular
time interval the master digital file is updated; generate a file
update prediction for discouraging offline_use of an outdated
version of the master digital file, the file update prediction
indicating an estimation for when the master digital file is likely
to be updated based on the pattern of updates; and generate an
alert for display offline on a user interface of the first
computing device, the alert indicating the estimation for when the
master digital will be updated by the one or more users to
discourage and avoid use of the outdated version of the master
digital file by the user.
16. A system as in claim 15, further comprising program instruction
to: analyze applications resident on the first computing device and
the one or more second computing devices for user data of the one
or more users; and updating the pattern of updates if the user data
of the one or more users indicates the pattern of updates will be
interrupted.
17. A system as in claim 16, wherein the user data includes user
application data from at least one of the group of user
applications consisting of: a user calendar, a user social media
account, or a user schedule.
18. A system as in claim 15, wherein the alert is a time estimation
comprising a countdown clock to when the master digital file will
be updated by the one or more users.
19. A system as in claim 16, wherein the metadata includes
information as to when the master digital file has been updated and
the one or more users who updated the master digital file.
20. A system as in claim 15, wherein the alert is a visual
notification.
Description
BACKGROUND
[0001] The present invention relates generally to a method, system,
and computer program for alerting an offline user using a
downloaded digital file of a predicted computer file update of a
master file, so that the user can go online and retrieve the
updated master file. More particularly, the present invention
relates to a method, system, and computer program for predicting
the probability and timing of an update to a master digital
file.
[0002] Digital files may contain metadata, which may convey certain
details about the digital file. For example, digital file metadata
can contain a history of when the digital file was created, the
date the digital file was last modified, or when the date the
digital file was downloaded. Further, digital files may be stored
on a server where several users have access to the digital file.
The digital file stored on server to which several users have
access to may be referred to as a master digital file, as this file
serves as the main digital file. Users may then download a copy of
the master digital file for use offline. The offline copy of the
master digital file can be modified and then uploaded to the server
resulting in an updated master digital file.
[0003] In the above example, once a user has downloaded a copy of a
master digital file, users have no guidelines as to whether their
downloaded digital file copy is the most up-to-date version of that
digital file or if the master digital file was updated by another
user during the time the user was updating or editing the offline
copy of the digital file. Typically, an offline user does not know
whether their downloaded copy of the master digital file is the
most current version of the master digital file and if any updates
to the downloaded copy of the master digital file are still
relevant or necessary. Therefore, an offline user has no way of
knowing that he/she may be working on an outdated version of the
master digital file. Also, a user may unknowingly update an
outdated copy of a master digital file and then upload it,
replacing a newer version of the master digital file with an
updated older version. Thus, an offline user may cause confusion
amongst a group of users sharing the master digital file by
uploading outdated versions of the master digital file and
potentially losing data added in newer versions. Further, a user
who has downloaded a copy of the master digital file for use
offline may use or rely on the information in a master file which
is outdated.
BRIEF SUMMARY
[0004] An embodiment of the invention may include a method,
computer program product and computer system for predicting a
digital file update. The method, computer program product and
computer system may include computing device which may receive a
copy of a master digital file from a server. The master digital
file may include metadata. The computing device may analyze the
metadata of the master digital file for a pattern of updates
initiated by one or more users using one or more computing devices.
The computing device may generate a file update prediction for
discouraging use of an outdated version of the master digital file.
The file update prediction may indicate an estimation for when the
master digital file is likely to be updated based on the pattern of
updates. The computing device may generate an alert for display
offline on a user interface. The alert may indicate the estimation
for when the master digital will be updated by the one or more
users to discourage and avoid use of the outdated version of the
master digital file by the user.
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] FIG. 1 illustrates a system for a digital file update
prediction and alerting a user, in accordance with an embodiment of
the invention.
[0006] FIG. 2 is a flowchart illustrating an example method of the
digital file update prediction, in accordance with an embodiment of
the invention.
[0007] FIG. 3 is a flowchart illustrating an example method of the
digital file update prediction, in accordance with an embodiment of
the invention.
[0008] FIG. 4 is a flowchart illustrating an example method of the
digital file update prediction, in accordance with an embodiment of
the invention.
[0009] FIG. 5 is a flowchart illustrating an example method of the
digital file update prediction, in accordance with an embodiment of
the invention.
[0010] FIG. 6 is a block diagram depicting the hardware components
of the digital file update prediction system of FIG. 1, in
accordance with an embodiment of the invention.
[0011] FIG. 7 illustrates a cloud computing environment, in
accordance with an embodiment of the invention.
[0012] FIG. 8 illustrates a set of functional abstraction layers
provided by the cloud computing environment of FIG. 7, in
accordance with an embodiment of the invention.
DETAILED DESCRIPTION
[0013] Embodiments of the present invention will now be described
in detail with reference to the accompanying Figures.
[0014] The following description with reference to the accompanying
drawings is provided to assist in a comprehensive understanding of
exemplary embodiments of the invention as defined by the claims and
their equivalents. It includes various specific details to assist
in that understanding but these are to be regarded as merely
exemplary. Accordingly, those of ordinary skill in the art will
recognize that various changes and modifications of the embodiments
described herein can be made without departing from the scope and
spirit of the invention. In addition, descriptions of well-known
functions and constructions may be omitted for clarity and
conciseness.
[0015] The terms and words used in the following description and
claims are not limited to the bibliographical meanings, but, are
merely used to enable a clear and consistent understanding of the
invention. Accordingly, it should be apparent to those skilled in
the art that the following description of exemplary embodiments of
the present invention is provided for illustration purpose only and
not for the purpose of limiting the invention as defined by the
appended claims and their equivalents.
[0016] It is to be understood that the singular forms "a," "an,"
and "the" include plural referents unless the context clearly
dictates otherwise. Thus, for example, reference to "a component
surface" includes reference to one or more of such surfaces unless
the context clearly dictates otherwise.
[0017] The present invention provides a method, computer program,
and computer system for predicting the probability and timing of an
update to a master digital file and providing an alert to a user
when the master digital file is likely to be updated. In an
embodiment of the present invention, the metadata associated with a
master digital file may be analyzed to determine the probability
and timing of an update to a master digital file. The present
invention may analyze the master digital file and the associated
metadata, using the inventive program, while the user device
running the inventive program is connected to a communications
network. Thus, the master digital file and the associated metadata
may be analyzed before or during the download of the master digital
file by a user. Alternatively, the present invention may analyze
the master digital file and the associated metadata, using the
inventive program, after the master digital file has been
downloaded to the user's device.
[0018] In another embodiment of the present invention, the
inventive program may analyze certain user data associated with all
the users who have access to the master digital file. The user data
may include data from the users' calendar applications, scheduling
applications, file sharing applications, social media applications,
email applications, and instant messaging applications, etc. The
present invention may analyze the users' data, using the inventive
program, while the user device running the inventive program is
connected to a communications network. Thus, the users' data may be
analyzed before or during the download of the master digital file
by a user. Alternatively, the present invention may download the
users' data, using the inventive program, to the user device and
then analyze the users' data after the users' data has been
downloaded to the user's device.
[0019] Reference will now be made in detail to the embodiments of
the present invention, examples of which are illustrated in the
accompanying drawings, wherein like reference numerals refer to
like elements throughout. Embodiments of the invention are
generally directed to a system for analyzing, predicting, and
alerting an offline user as to the update status of a master
digital file.
[0020] FIG. 1 illustrates a file update prediction system 100, in
accordance with an embodiment of the invention. In an example
embodiment, file update prediction system 100 includes a content
device 110, and user devices 120a, 120b interconnected via network
140.
[0021] In the example embodiment, the network 140 is the Internet,
representing a worldwide collection of networks and gateways to
support communications between devices connected to the
[0022] Internet. The network 140 may include, for example, wired,
wireless or fiber optic connections. In other embodiments, the
network 140 may be implemented as an intranet, a local area network
(LAN), or a wide area network (WAN). In general, the network 140
can be any combination of connections and protocols that will
support communications between the content device 110, and the user
device 120.
[0023] The content device 110 may include a content database 112.
In the example embodiment, the content device 110 may be a desktop
computer, a notebook, a laptop computer, a tablet computer, a thin
client, or any other electronic device or computing system capable
of storing compiling and organizing audio, visual, or textual
content and receiving and sending that content to and from other
computing devices, such as the user device 120 via the network 140.
In some embodiments, the content device 110 includes a collection
of devices or data sources. The content device 110 is described in
more detail with reference to FIG. 4.
[0024] The content database 112 may be a collection of digital
files including, but not limited to, audio, visual, and textual
files. For example, the content database 112 may include text
files, spreadsheets, PDF files, or any other digital file type
which may be edited by a user or group of users. The collection of
digital files on the content database 112 may include a master
digital file 114. The master digital file 114 may be a digital file
shared between one or more users of the user devices 120a, 120b.
The master digital file 114 may include metadata 116. The metadata
116 may include, but is not limited to, history of digital file
access, history of digital file updates, users who have accessed
the digital files, users who have updated the digital file, digital
file creation date, etc. The content database 112 located on the
content device 110 can be accessed through using the network
140.
[0025] The user devices 120a, 120b may include file update
prediction programs 122a, 122b, user databases 128a, 128b, and
applications 130a, 130b. In the example embodiment, the user
devices 120a, 120b may be a desktop computer, a notebook, a laptop
computer, a tablet computer, a thin client, or any other electronic
device or computing system capable of storing compiling and
organizing audio, visual, or textual content and receiving and
sending that content to and from other computing devices, such as
the content device 110, and other user devices 120 via the network
140. The user devices 120a, 120b are described in more detail with
reference to FIG. 4. While only two user devices 120a-b are
illustrated, it can be appreciated that any number of user devices
120 may be part of the file update prediction system 100. Further,
while reference to only one user device 120a or 120b may be used in
this description, it is understood that reference to one user
device and any components associated with such user device, such as
user device 120a or 120b and their respective components, applies
to any and all user devices and their associated components unless
otherwise specified.
[0026] The file update prediction program 122a may include
prediction algorithms 124a, 124b, alert generators 125a, 125b and
user interfaces 126a, 126b. The file update prediction program 122a
is a program capable of analyzing an original digital file, i.e.
the master digital file 114, that has been selected to be
downloaded by user for use offline and predict when the master
digital file 114 will be updated next. The file update prediction
program 122a may analyze metadata 116 and/or historical data
associated with the master digital file 114 using the prediction
algorithm 124a to predict when the next update to the master
digital file 114 will be. The metadata 116 and/or historical data
may include, but is not limited to, history of digital file access,
history of digital file updates, users who have accessed the
digital files, users who have updated the digital file, digital
file creation date, etc. The file update predication program 122a
may analyze the metadata 116 and/or the historical data associated
with the master digital file 114 while the user device 120a is
connected to the network 140 or the user device 120a may download
the metadata 116 and/or the historical data associated with the
master digital file 114 while connected to the network 140 and
analyze offline. For example, the file update prediction program
122a may analyze the historical data of the master digital file 114
and determine that the master digital file 114 is being updated
once a week, but on a different day each week. The file update
prediction program 122a will then run the prediction algorithm 124a
that will predict a date and time that the downloaded copy of the
master digital file 114 will no longer be the same as the master
digital file. Continuing with the example above where the file
prediction program 122a determines that the master digital file 114
is being updated once a week on a different day, the prediction
algorithm 124a may predict that the master digital file 114 will be
updated on Monday, the first day of the following week.
Alternatively, the file prediction program 122a may determine that
the earliest the master digital file 114 has ever been updated was
a Wednesday; thus, prediction algorithm 124a may predict that the
master digital file 114 will be updated on the Wednesday of the
following week. Further, the file update prediction program 122a
may analyze data from the application 130 located on the user
device 120a to predict when the master digital file 114 is likely
to be updated next. For example, the application 130a may be a user
calendar application. The file update prediction program 122a may
analyze the user calendar application to determine if the update of
the master digital file 114 will remain on schedule or be
interrupted because, for example, a user will be away on vacation.
Continuing with the example above where the file prediction program
122a determines that the master digital file 114 is being updated
once a week on a different day by a user on the user device 120b,
the file prediction program 122a may further determine from
analysis of the application 130b that a user of user device 120b
who normally updates the master digital file 114 is on vacation for
the next two weeks. Thus, prediction algorithm 124a may predict,
for a user on the user device 120a, that the file will be updated
on Monday, two weeks after a copy of the master digital file 114
has been downloaded. The application 130a is described in more
detail below. Further, once the file update prediction program 122a
has predicted when the master digital file 114 will be updated
next, the file update prediction program 122a may output a display
using the alert generator 125a to a user of the user device 120a in
association with the downloaded digital file to indicate how long
until the master digital file 114 is updated. For example, the
alert generator 125a of the file update prediction program 122a may
display an offline alert.
[0027] The prediction algorithm 124a may be any algorithm capable
of analyzing digital files, the associated metadata 116 and
historical data with those digital files, and data associated with
application 130a to calculate the probability of when the digital
file will be updated next. The prediction algorithm 124a may
utilize, for example, but not limited to, linear discriminant
analysis (LDA) to ascertain the optimum heartbeat, or "keepalive",
on a digital file. For example, the prediction algorithm 124a may
create a graphical representation of the data, for example, a heat
map, illustrating groupings of data based on, but not limited to,
historical interactions between digital file users of a digital
file, user downloads and access of a digital file, updates of a
digital file. Prediction algorithm 124a may analyze these heat map
groupings to determine the dominant heat group, i.e. cluster, and
based on that cluster, predict when a digital file will be
updated.
[0028] The user interface 126a includes components used to receive
input from a user on the user device 120a and transmit the input to
the file update prediction program 122a, or conversely to receive
information from the file update prediction program 122a and
display the information to the user on the user device 120a. In an
example embodiment, the user interface 126a uses a combination of
technologies and devices, such as device drivers, to provide a
platform to enable users of user device 120a to interact with the
file update prediction program 122a. In the example embodiment,
user interface 126a receives input, such as textual input received
from a physical input device, such as a keyboard.
[0029] User database 128a may include digital files stored for use
by user device 120a such as the copy of the master digital file 114
downloaded by the user to user device 110a. Digital files may be
created on user device 120a and stored on user database 128a or the
digital files may be downloaded from another computing device, such
as, but not limited to, the content device 110. Digital files may
include, but is not limited to, audio, visual, and textual content.
For example, user database 128a may contain a text document that
has been created by a user of the user device 120a. Further, the
text document created by a user of the user device 120a and stored
on the user database 128a, may be shared and edited by user of the
user device 120b. In another example embodiment, the user database
128a may contain a text document downloaded from the content device
110 over the network 140. Further, the text document downloaded
from the content device 110 may be shared and edited by users of
the user devices 120a, 120b.
[0030] The application 130a may be any computer application which
has information relating to the availability or activity of a user
such as, but not limited to, calendar applications, scheduling
applications, file sharing applications, social media applications,
email applications, and instant messaging applications, etc. As
stated above, application 130a may be a user's calendar
application. Thus, the file update prediction program 122a may use
data associated with the application 130a to help predict when the
master digital file 114 will be updated. For example, the master
digital file 114 may be stored on the content database 112 and
shared between two users on the user devices 120a and 120b and
updated periodically by the first user on the user device 120a. The
second user on the user device 120b may want to download a copy of
the master digital file 114 for use offline. Before a copy of the
master digital file 114 is downloaded, the file update prediction
program 122a will analyze the master digital file 114 including
application 130a resident on the user device 120a of the first user
to predict when the master digital file 114 will be updated. For
example, the file update prediction program 122a may find that the
master digital file 114 is updated once a week by the first user on
different day each week, but that the first user will be away on
vacation for the next week based on the application 130a. Thus, the
file update prediction program 122a may predict that the master
digital file 114 will not be updated on its regular schedule but
will be updated in one week when the first user returns from
vacation. While only a single application 130a is illustrated, it
can be appreciated that the user device 120a may include multiple
applications.
[0031] Referring to FIG. 2, a method 200 for file update prediction
is depicted, in accordance with an embodiment of the present
invention.
[0032] Referring to block 210, the master digital file 114 is
stored on the content database 112 by a user on the user device
120a. The master digital file 114 being stored on the content
database 112 may be a new master digital file or it may be an
updated master digital file. For example, a user on the user device
120a may have downloaded a copy of the master digital file 114 from
the content database 112 over network 140, modified the copy and is
now uploading the copy to update the master digital file 114 on the
content database 112. Thus, content database 112 is storing an
updated version of the master digital file 114 already stored on
content database 112. Alternatively, a user on the user device 120a
may have created a new digital file and is now saving that new
digital file as the master digital file 114 to the content database
112 so that other users on user device 120a may access the new
master digital file 114.
[0033] Referring to block 212, the user device 120a receives a copy
of the master digital file 114 stored on content database 112 over
network 140. For example, a user on device 120a may request to
download a copy of the master digital file 114 stored on content
database 112 for use offline.
[0034] Referring to block 214, the digital file is analyzed by the
file update prediction program 122a using the prediction algorithm
124a. The file update prediction program 122a analyzes the master
digital file 114 while the user is still connected to network 140.
For example, the file update prediction program 122a may analyze
data associated with the master digital file 114, such as, but not
limited to, the metadata 116, and the historical data as described
in more detail above with reference to FIG. 1. Referring to block
216, the file update prediction program 122a generates a file
update prediction for the master digital file 114. The file update
prediction may be based on the analysis of the master digital file
114 performed at block 214. The file update prediction may include
the date and time the master digital file 114 stored on content
database 112 will be updated next. For example, the file update
prediction program 122a may determine that the master digital file
114 stored on the content database 112 is updated every Friday at
4:00 p.m. In the preceding example, the file update prediction
program 122a will generate a file update prediction that the master
digital file 114 stored on the content database 112 will be updated
on the Friday following the date of download at 4:00 p.m. Thus, the
downloaded copy of the master digital file 114 will most likely no
longer be the most up-to-date version of that master digital file
114 as of the Friday following the date of download at 4:00
p.m.
[0035] Referring to block 218, the file update prediction program
122a using alert generator 125a creates an offline alert indicating
when the master digital file 114 is likely to be updated. The
offline alert may be any display notification, including but not
limited to, a visual notification, to indicate a time when the
master digital file 114 is due to be updated. For example, the
offline alert may be, but is not limited to, a timestamp to
indicate a time when the master digital file 114 would likely be
updated, or a countdown clock counting down to a time when the
master digital file 114 would likely be updated. Further, the
offline alert may include, but is not limited to, a notification on
the user interface 126a, a notification on the downloaded copy of
the master digital file 114, or a notification on the desktop of
the user device 120a, etc. In yet another embodiment of the
invention, the offline alert may be color coded to indicate the how
long is left until the master digital file 114 is updated. For
example, the offline alert may start off as green to indicate that
more than a threshold amount of time is left before the master
digital file 114 is due to be updated. The offline alert may turn
yellow as the threshold amount of time nears and the offline alert
may then turn red once the threshold amount of time has passed. In
the preceding example, the threshold amount of time is a
predetermined time before the master digital file 114 is due to be
update, for example, but not limited to, an hour, a day, two days,
etc. The threshold amount of time may be predetermined by the file
update prediction program 122a based on the analysis performed at
block 214 such that the threshold amount of time is tied to the
file update prediction. Thus, the alert may prevent a user from
updating an old version of the master digital file 114 or replacing
a newer version of the master digital file 114 uploaded by another
user with an updated older version of the master digital file 114.
Further, the alert may prevent a user from using or relying on
outdated or incorrect information contained in an outdated version
of the master digital file 114.
[0036] Referring to block 220, a copy of the master digital file
114 stored on content database 112 is downloaded to user device
120a. In an embodiment of the invention, a copy of the master
digital file 114 may be downloaded to user database 128a.
[0037] Referring to FIG. 3, another example method 300 for file
update prediction is depicted, in accordance with an embodiment of
the present invention. The embodiment of FIG. 3 is substantially
similar to that of FIG. 2 with blocks 310-312 being the same as
blocks 210-212, block 314 being the same as block 220, and blocks
316-320 being the same as blocks 214-218. Thus, the embodiment
illustrated by method 300 allows for a copy of a master digital
file 114 and the associated metadata 116 to be downloaded to the
user device 120a by the file update prediction program 122a before
the file update prediction program 122a analyzes the metadata 116,
generates a file update prediction, and creates an offline alert.
The embodiment of FIG. 3 may be understood with reference to FIG.
2.
[0038] Referring to FIG. 4, another example method file update
prediction is depicted, in accordance with an embodiment of the
present invention. The embodiment of FIG. 4 is substantially
similar to that of FIG. 2 with blocks 410-414 being the same as
blocks 210-214 and blocks 418-422 being the same as blocks 216-220.
Thus, FIG. 4 is the same as FIG. 2, with block 416 added. The
embodiment of FIG. 4 may be understood with reference to FIG.
2.
[0039] Referring to block 318, the file update prediction program
122 analyzes user data associated with the application 130a on user
device 120a. While only a single application 130a is illustrated,
the file update prediction program 122a may analyze one or more
applications on user device 120a. Further, the file update
prediction program 122a may analyze one or more of the user device
120a. For example, the master digital file 114 may be shared
between two people on the user devices 120a, 120b. Thus, the file
update prediction program 122a would analyze the applications 130a,
130b or several applications on each of the user devices 120a,
120b.
[0040] Referring to FIG. 5, another example method 500 for file
update prediction is depicted, in accordance with an embodiment of
the present invention. The embodiment of FIG. 5 is substantially
similar to that of FIG. 4 with blocks 510-512 being the same as
blocks 410-412, block 514 being the same as block 422, and blocks
516-522 being the same as blocks 414-420. Thus, the embodiment
illustrated by method 500 allows for a copy of the master digital
file 114 document and the associated metadata 116 to be downloaded
the user device 120a before the file update prediction program 122a
analyzes the associated metadata 116 and user data, generates a
file update prediction, and creates an offline alert. The
embodiment of FIG. 5 may be understood with reference to FIG.
4.
[0041] Referring to FIG. 6, a system 1000 includes a computer
system or computer 1010 shown in the form of a generic computing
device. The methods 200, 300, 400, and 500, for example, may be
embodied in a program(s) 1060 (FIG. 6) embodied on a computer
readable storage device, for example, generally referred to as
memory 1030 and more specifically, computer readable storage medium
1050 as shown in FIG. 6. For example, memory 1030 can include
storage media 1034 such as RAM (Random Access Memory) or ROM (Read
Only Memory), and cache memory 1038. The program 1060 is executable
by the processing unit or processor 1020 of the computer system
1010 (to execute program steps, code, or program code). Additional
data storage may also be embodied as a database 1110 which can
include data 1114. The computer system 1010 and the program 1060
shown in FIG. 7 are generic representations of a computer and
program that may be local to a user, or provided as a remote
service (for example, as a cloud based service), and may be
provided in further examples, using a website accessible using the
communications network 1200 (e.g., interacting with a network, the
Internet, or cloud services). It is understood that the computer
system 1010 also generically represents herein a computer device or
a computer included in a device, such as a laptop or desktop
computer, etc., or one or more servers, alone or as part of a
datacenter. The computer system can include a network
adapter/interface 1026, and an input/output (I/O) interface(s)
1022. The I/O interface 1022 allows for input and output of data
with an external device 1074 that may be connected to the computer
system. The network adapter/interface 1026 may provide
communications between the computer system a network generically
shown as the communications network 1200.
[0042] The computer 1010 may be described in the general context of
computer system-executable instructions, such as program modules,
being executed by a computer system. Generally, program modules may
include routines, programs, objects, components, logic, data
structures, and so on that perform particular tasks or implement
particular abstract data types. The method steps and system
components and techniques may be embodied in modules of the program
1060 for performing the tasks of each of the steps of the method
and system. The modules are generically represented in FIG. 6 as
program modules 1064. The program 1060 and program modules 1064 can
execute specific steps, routines, sub-routines, instructions or
code, of the program.
[0043] The method of the present disclosure can be run locally on a
device such as a mobile device, or can be run a service, for
instance, on the server 1100 which may be remote and can be
accessed using the communications network 1200. The program or
executable instructions may also be offered as a service by a
provider. The computer 1010 may be practiced in a distributed cloud
computing environment where tasks are performed by remote
processing devices that are linked through a communications network
1200. In a distributed cloud computing environment, program modules
may be located in both local and remote computer system storage
media including memory storage devices.
[0044] More specifically, as shown in FIG. 6, the system 1000
includes the computer system 1010 shown in the form of a
general-purpose computing device with illustrative periphery
devices. The components of the computer system 1010 may include,
but are not limited to, one or more processors or processing units
1020, a system memory 1030, and a bus 1014 that couples various
system components including system memory 1030 to processor
1020.
[0045] The bus 1014 represents one or more of any of several types
of bus structures, including a memory bus or memory controller, a
peripheral bus, an accelerated graphics port, and a processor or
local bus using any of a variety of bus architectures. By way of
example, and not limitation, such architectures include Industry
Standard Architecture (ISA) bus, Micro Channel Architecture (MCA)
bus, Enhanced ISA (EISA) bus, Video Electronics Standards
Association (VESA) local bus, and Peripheral Component
Interconnects (PCI) bus.
[0046] The computer 1010 can include a variety of computer readable
media. Such media may be any available media that is accessible by
the computer 1010 (e.g., computer system, or server), and can
include both volatile and non-volatile media, as well as, removable
and non-removable media. Computer memory 1030 can include
additional computer readable media 1034 in the form of volatile
memory, such as random access memory (RAM), and/or cache memory
1038. The computer 1010 may further include other
removable/non-removable, volatile/non-volatile computer storage
media, in one example, portable computer readable storage media
1072. In one embodiment, the computer readable storage medium 1050
can be provided for reading from and writing to a non-removable,
non-volatile magnetic media. The computer readable storage medium
1050 can be embodied, for example, as a hard drive. Additional
memory and data storage can be provided, for example, as the
storage system 1110 (e.g., a database) for storing data 1114 and
communicating with the processing unit 1020. The database can be
stored on or be part of a server 1100. Although not shown, a
magnetic disk drive for reading from and writing to a removable,
non-volatile magnetic disk (e.g., a "floppy disk"), and an optical
disk drive for reading from or writing to a removable, non-volatile
optical disk such as a CD-ROM, DVD-ROM or other optical media can
be provided. In such instances, each can be connected to bus 1014
by one or more data media interfaces. As will be further depicted
and described below, memory 1030 may include at least one program
product which can include one or more program modules that are
configured to carry out the functions of embodiments of the present
invention.
[0047] The methods 200, 300, 400, and 500 (FIGS. 2-5), for example,
may be embodied in one or more computer programs, generically
referred to as a program(s) 1060 and can be stored in memory 1030
in the computer readable storage medium 1050. The program 1060 can
include program modules 1064. The program modules 1064 can
generally carry out functions and/or methodologies of embodiments
of the invention as described herein. The one or more programs 1060
are stored in memory 1030 and are executable by the processing unit
1020. By way of example, the memory 1030 may store an operating
system 1052, one or more application programs 1054, other program
modules, and program data on the computer readable storage medium
1050. It is understood that the program 1060, and the operating
system 1052 and the application program(s) 1054 stored on the
computer readable storage medium 1050 are similarly executable by
the processing unit 1020.
[0048] The computer 1010 may also communicate with one or more
external devices 1074 such as a keyboard, a pointing device, a
display 1080, etc.; one or more devices that enable a user to
interact with the computer 1010; and/or any devices (e.g., network
card, modem, etc.) that enables the computer 1010 to communicate
with one or more other computing devices. Such communication can
occur via the Input/Output (I/O) interfaces 1022. Still yet, the
computer 1010 can communicate with one or more networks 1200 such
as a local area network (LAN), a general wide area network (WAN),
and/or a public network (e.g., the Internet) via network
adapter/interface 1026. As depicted, network adapter 1026
communicates with the other components of the computer 1010 via bus
1014. It should be understood that although not shown, other
hardware and/or software components could be used in conjunction
with the computer 1010. Examples, include, but are not limited to:
microcode, device drivers 1024, redundant processing units,
external disk drive arrays, RAID systems, tape drives, and data
archival storage systems, etc.
[0049] It is understood that a computer or a program running on the
computer 1010 may communicate with a server, embodied as the server
1100, via one or more communications networks, embodied as the
communications network 1200. The communications network 1200 may
include transmission media and network links which include, for
example, wireless, wired, or optical fiber, and routers, firewalls,
switches, and gateway computers. The communications network may
include connections, such as wire, wireless communication links, or
fiber optic cables. A communications network may represent a
worldwide collection of networks and gateways, such as the
Internet, that use various protocols to communicate with one
another, such as Lightweight Directory Access Protocol (LDAP),
Transport Control Protocol/Internet Protocol (TCP/IP), Hypertext
Transport Protocol (HTTP), Wireless Application Protocol (WAP),
etc. A network may also include a number of different types of
networks, such as, for example, an intranet, a local area network
(LAN), or a wide area network (WAN).
[0050] In one example, a computer can use a network which may
access a website on the Web (World Wide Web) using the Internet. In
one embodiment, a computer 1010, including a mobile device, can use
a communications system or network 1200 which can include the
Internet, or a public switched telephone network (PSTN) for
example, a cellular network. The PSTN may include telephone lines,
fiber optic cables, microwave transmission links, cellular
networks, and communications satellites. The Internet may
facilitate numerous searching and texting techniques, for example,
using a cell phone or laptop computer to send queries to search
engines via text messages (SMS), Multimedia Messaging Service (MMS)
(related to SMS), email, or a web browser. The search engine can
retrieve search results, that is, links to websites, documents, or
other downloadable data that correspond to the query, and
similarly, provide the search results to the user via the device
as, for example, a web page of search results.
[0051] It is to be understood that although this disclosure
includes a detailed description on cloud computing, implementation
of the teachings recited herein are not limited to a cloud
computing environment. Rather, embodiments of the present invention
are capable of being implemented in conjunction with any other type
of computing environment now known or later developed.
[0052] Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g., networks, network
bandwidth, servers, processing, memory, storage, applications,
virtual machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0053] Characteristics are as follows:
[0054] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0055] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0056] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but may
be able to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter).
[0057] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0058] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (e.g.,
storage, processing, bandwidth, and active user accounts). Resource
usage can be monitored, controlled, and reported, providing
transparency for both the provider and consumer of the utilized
service.
Service Models are as follows:
[0059] Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0060] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0061] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
[0062] Deployment Models are as follows:
[0063] Private cloud: the cloud infrastructure is operated solely
for an organization. It may be managed by the organization or a
third party and may exist on-premises or off-premises.
[0064] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
[0065] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0066] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together by standardized or
proprietary technology that enables data and application
portability (e.g., cloud bursting for load-balancing between
clouds).
[0067] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure that includes a network of interconnected nodes.
[0068] Referring now to FIG. 5, illustrative cloud computing
environment 50 is depicted. As shown, cloud computing environment
50 includes one or more cloud computing nodes 10 with which local
computing devices used by cloud consumers, such as, for example,
personal digital assistant (PDA) or cellular telephone 54A, desktop
computer 54B, laptop computer 54C, and/or automobile computer
system 54N may communicate. Nodes 10 may communicate with one
another. They may be grouped (not shown) physically or virtually,
in one or more networks, such as Private, Community, Public, or
Hybrid clouds as described hereinabove, or a combination thereof.
This allows cloud computing environment 50 to offer infrastructure,
platforms and/or software as services for which a cloud consumer
does not need to maintain resources on a local computing device. It
is understood that the types of computing devices 54A-N shown in
FIG. 9 are intended to be illustrative only and that computing
nodes 10 and cloud computing environment 50 can communicate with
any type of computerized device over any type of network and/or
network addressable connection (e.g., using a web browser).
[0069] Referring now to FIG. 6, a set of functional abstraction
layers provided by cloud computing environment 50 (FIG. 5) is
shown. It should be understood in advance that the components,
layers, and functions shown in FIG. 10 are intended to be
illustrative only and embodiments of the invention are not limited
thereto. As depicted, the following layers and corresponding
functions are provided:
[0070] Hardware and software layer 60 includes hardware and
software components. Examples of hardware components include:
mainframes 61; RISC (Reduced Instruction Set Computer) architecture
based servers 62; servers 63; blade servers 64; storage devices 65;
and networks and networking components 66. In some embodiments,
software components include network application server software 67
and database software 68.
[0071] Virtualization layer 70 provides an abstraction layer from
which the following examples of virtual entities may be provided:
virtual servers 71; virtual storage 72; virtual networks 73,
including virtual private networks; virtual applications and
operating systems 74; and virtual clients 75.
[0072] In one example, management layer 80 may provide the
functions described below. Resource provisioning 81 provides
dynamic procurement of computing resources and other resources that
are utilized to perform tasks within the cloud computing
environment. Metering and Pricing 82 provide cost tracking as
resources are utilized within the cloud computing environment, and
billing or invoicing for consumption of these resources. In one
example, these resources may include application software licenses.
Security provides identity verification for cloud consumers and
tasks, as well as protection for data and other resources. User
portal 83 provides access to the cloud computing environment for
consumers and system administrators. Service level management 84
provides cloud computing resource allocation and management such
that required service levels are met. Service Level Agreement (SLA)
planning and fulfillment 85 provide pre-arrangement for, and
procurement of, cloud computing resources for which a future
requirement is anticipated in accordance with an SLA.
[0073] Workloads layer 90 provides examples of functionality for
which the cloud computing environment may be utilized. Examples of
workloads and functions which may be provided from this layer
include: mapping and navigation 91; software development and
lifecycle management 92; virtual classroom education delivery 93;
data analytics processing 94; transaction processing 95; and file
update prediction 96.
[0074] The programs described herein are identified based upon the
application for which they are implemented in a specific embodiment
of the invention. However, it should be appreciated that any
particular program nomenclature herein is used merely for
convenience, and thus the invention should not be limited to use
solely in any specific application identified and/or implied by
such nomenclature.
[0075] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of code, which comprises one or more
executable instructions for implementing the specified logical
function(s). It should also be noted that, in some alternative
implementations, the functions noted in the block may occur out of
the order noted in the figures. For example, two blocks shown in
succession may, in fact, be executed substantially concurrently, or
the blocks may sometimes be executed in the reverse order,
depending upon the functionality involved. It will also be noted
that each block of the block diagrams and/or flowchart
illustration, and combinations of blocks in the block diagrams
and/or flowchart illustration, can be implemented by special
purpose hardware-based systems that perform the specified functions
or acts, or combinations of special purpose hardware and computer
instructions.
[0076] The present invention may be a system, a method, and/or a
computer program product at any possible technical detail level of
integration. The computer program product may include a computer
readable storage medium (or media) having computer readable program
instructions thereon for causing a processor to carry out aspects
of the present invention.
[0077] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0078] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0079] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, configuration data for integrated
circuitry, or either source code or object code written in any
combination of one or more programming languages, including an
object oriented programming language such as Smalltalk, C++, or the
like, and procedural programming languages, such as the "C"
programming language or similar programming languages. The computer
readable program instructions may execute entirely on the user's
computer, partly on the user's computer, as a stand-alone software
package, partly on the user's computer and partly on a remote
computer or entirely on the remote computer or server. In the
latter scenario, the remote computer may be connected to the user's
computer through any type of network, including a local area
network (LAN) or a wide area network (WAN), or the connection may
be made to an external computer (for example, through the Internet
using an Internet Service Provider). In some embodiments,
electronic circuitry including, for example, programmable logic
circuitry, field-programmable gate arrays (FPGA), or programmable
logic arrays (PLA) may execute the computer readable program
instructions by utilizing state information of the computer
readable program instructions to personalize the electronic
circuitry, in order to perform aspects of the present
invention.
[0080] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0081] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0082] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0083] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the blocks may occur out of the order noted in
the Figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0084] While steps of the disclosed method and components of the
disclosed systems and environments have been sequentially or
serially identified using numbers and letters, such numbering or
lettering is not an indication that such steps must be performed in
the order recited, and is merely provided to facilitate clear
referencing of the method's steps. Furthermore, steps of the method
may be performed in parallel to perform their described
functionality.
* * * * *